The influence of short-term temporal variability on the efficacy of dragonfly larvae as mercury biosentinels

Abstract

Mercury (Hg) exposure to fish, wildlife, and humans is widespread and of global concern, thus stimulating efforts to reduce emissions. Because the relationships between rates of inorganic Hg loading, methylmercury (MeHg) production, and bioaccumulation are extremely complex and challenging to predict, there is a need for reliable biosentinels to understand the distribution of Hg in the environment and monitor the effectiveness of reduction efforts. However, it is important to assess how temporal and spatial variation at multiple scales influences the efficacy of specific biosentinels. Seasonal and interannual variation in total Hg (THg) concentrations of dragonfly larvae were examined in relation to spatial variability among 21 sites in two U.S. national parks with contrasting ecologies and Hg deposition patterns. Dragonfly THg differed among sampling events at 17 of the 21 sites, but by an average of only 20.4 % across events, compared to an average difference of 52.7 % among sites. Further, THg concentrations did not follow consistent seasonal patterns across sites or years, suggesting that the observed temporal variation was unlikely to bias monitoring efforts. Importantly, for a specific site, there was no difference in % MeHg in dragonflies among sampling events. Finally, there was significant temporal variability in the biogeochemical factors (aqueous inorganic Hg, aqueous MeHg, DOC, SO4, and pH) influencing dragonfly THg, with the importance of individual factors varying by 2.4 to 4.3-fold across sampling events. Despite these results, it is noteworthy that the observed temporal variation in dragonfly THg concentrations was neither large nor consistent enough to bias spatial assessments. Thus, although this temporal variation may provide insights into the processes influencing biological Hg concentrations, it is unlikely to impair the use of dragonflies as biosentinels for monitoring spatial or temporal patterns at scales relevant to most mitigation efforts.